A geomagnetic vector compensation method compatible with nonlinear interferences based on back propagation network and 3D Helmholtz coil.

Magnetic interferential compensation plays a vital role in geomagnetic vector measurement applications. Traditional compensation accounts for only the permanent interferences, induced field interferences, and eddy-current interferences. However, nonlinear magnetic interferences are found, which also have a great impact on measurement, and it cannot be fully characterized by a linear compensation model. This paper proposes a new compensation method based on a back propagation neural network, which can reduce the influence of the linear model on compensation accuracy due to its good nonlinear mapping capabilities. The high-quality network training requires representative datasets, yet it is a common problem in the engineering field. To provide adequate data, this paper adopts a 3D Helmholtz coil to restore the magnetic signal of a geomagnetic vector measurement system. A 3D Helmholtz coil is more flexible and practical than the geomagnetic vector measurement system itself when generating abundant data under different postures and applications. Simulations and experiments are both conducted to prove the superiority of the proposed method. According to the experiment, the proposed method can reduce the root mean square errors of north, east, and vertical components and the total intensity from 73.25, 68.54, 70.45, and 101.77 nT to 23.35, 23.58, 27.42, and 29.72 nT, respectively, compared with the traditional method.

Whole-genome sequence association study identifies cyclin dependent kinase 8 as a key gene for the number of mummified piglets.

OBJECTIVE: Pigs, an ideal biomedical model for human diseases, suffer from about 50% early embryonic and fetal death, a major cause of fertility loss worldwide. However, identifying the causal variant remains a huge challenge. This study aimed to detect single nucleotide polymorphisms (SNPs) and candidate genes for the number of mummified (NM) piglets using the imputed whole-genome sequence (WGS) and validate the potential candidate genes. METHODS: The imputed WGS was introduced from genotyping-by-sequencing (GBS) using a multi-breed reference population. We performed genome-wide association studies (GWAS) for NM piglets at birth from a Landrace pig populatiGWAS peak located on SSC11: 0.10 to 7.11 Mbp (Top SNP, SSC11:1,889,658 bp; p = 9.98E-13) was identified in cyclin dependent kinase on. A total of 300 Landrace pigs were genotyped by GBS. The whole-genome variants were imputed, and 4,252,858 SNPs were obtained. Various molecular experiments were conducted to determine how the genes affected NM in pigs. RESULTS: A strong GWAS peak located on SSC11: 0.10 to 7.11 Mbp (Top SNP, SSC11:1,889,658 bp; p = 9.98E-13) was identified in cyclin dependent kinase 8 (CDK8) gene, which plays a crucial role in embryonic retardation and lethality. Based on the molecular experiments, we found that Y-box binding protein 1 (YBX1) was a crucial transcription factor for CDK8, which mediated the effect of CDK8 in the proliferation of porcine ovarian granulosa cells via transforming growth factor beta/small mother against decapentaplegic signaling pathway, and, as a consequence, affected embryo quality, indicating that this pathway may be contributing to mummified fetal in pigs. CONCLUSION: A powerful imputation-based association study was performed to identify genes associated with NM in pigs. CDK8 was suggested as a functional gene for the proliferation of porcine ovarian granulosa cells, but further studies are required to determine causative mutations and the effect of loci on NM in pigs.

Progress in Genomic Mating in Domestic Animals.

Selection is a continuous process that can influence the distribution of target traits in a population. From the perspective of breeding, elite individuals are selected for breeding, which is called truncated selection. With the introduction and application of the best linear unbiased prediction (BLUP) method, breeders began to use pedigree-based estimated breeding values (EBV) to select candidates for the genetic improvement of complex traits. Although truncated selection based on EBV can significantly improve the genetic progress, the genetic relationships between individuals with a high breeding value are usually closed, and the probability of being co-selected is greater, which will lead to a rapid increase in the level of inbreeding in the population. Reduced genetic variation is not conducive to long-term sustainable breeding, so a trade-off between genetic progress and inbreeding is required. As livestock and poultry breeding enters the genomic era, using genomic information to obtain optimal mating plans has formally been proposed by Akdemir et al., a method called genomic mating (GM). GM is more accurate and reliable than using pedigree information. Moreover, it can effectively control the inbreeding level of the population and achieve long-term and sustainable genetic gain. Hence, GM is more suitable for modern animal breeding, especially for local livestock and poultry breed conservation and genetic improvement. This review mainly summarized the principle of genomic mating, the methodology and usage of genomic mating, and the progress of its application in livestock and poultry.

Blockchain and K-Means Algorithm for Edge AI Computing.

The current development of blockchain, technically speaking, still faces many key problems such as efficiency and scalability issues, and any distributed system faces the problem of how to balance consistency, availability, and fault tolerance need to be solved urgently. The advantage of blockchain is decentralization, and the most important thing in a decentralized system is how to make nodes reach a consensus quickly. This research mainly discusses the blockchain and K-means algorithm for edge AI computing. The natural pan-central distributed trustworthiness of blockchain provides new ideas for designing the framework and paradigm of edge AI computing. In edge AI computing, multiple devices running AI algorithms are scattered across the edge network. When it comes to decentralized management, blockchain is the underlying technology of the Bitcoin system. Due to its characteristics of immutability, traceability, and consensus mechanism of transaction data storage, it has recently received extensive attention. Blockchain technology is essentially a public ledger. This is done by recording data related to trust management to this ledger. To collaboratively complete artificial intelligence computing tasks or jointly make intelligent group decisions, frequent communication is required between these devices. By integrating idle computing resources in an area, a distributed edge computing platform is formed. Users obtain benefits by sharing their computing resources, and nodes in need complete computing tasks through the shared platform. In view of the identity security problems faced in the sharing process, this article introduces blockchain technology to realize the trust between users. All participants must register a secure identity in the blockchain network and conduct transactions in this security system. A K-means algorithm suitable for edge environments is proposed to identify different degradation stages of equipment operation reflected by multiple types of data. Based on the prediction of the fault state for a single type of data, the algorithm uses the historical data of multiple types of data together with the prediction data to predict the fault stage. During the research process, the average optimization energy consumption of K-means algorithm is 14.6% lower than that of GA. On the basis of designing a resource allocation scheme based on blockchain, the problem of how the participants can realize reliable resource use according to the recorded data on the chain is studied. The article implements the verification of the legality of the use of blockchain resources. In addition, a control node is introduced to master the global real-time information of the network to provide data support for the user's choice.

Combining computer vision score and conventional meat quality traits to estimate the intramuscular fat content using machine learning in pigs.

Intramuscular fat content (IMF%) is an important factor that affects the quality of pork. The traditional testing method (Soxhlet extraction) is accurate; however, it has a long preprocessing time. In this study, a total of 1481 photographs of 200 pigs' loin muscles were used to obtain a computer vision score (IIMF %). Then, actual IMF%, meat color, marbling score, pH value, and drip loss of 200 pigs were measured. Stepwise regression (SR) and gradient boosting machine (GBM) were used to construct the estimation model of IMF%. The results showed that the correlation coefficients between IMF% and IIMF%, marbling score, backfat thickness, percentage of moisture (POM), and pH value were 0.68, 0.64, 0.48, 0.45, and 0.25, respectively. The model accuracies of SR and GBM base on residuals distribution were 0.875 and 0.89, respectively. This study presents a method for estimating IMF% using computer vision technology and meat quality traits.

Detecting the selection signatures in Chinese Duroc,Landrace, Yorkshire, Liangshan, and Qingyu pigs.

Here we used two kinds of chips data from 5 pig breeds, Chinese Duroc (DD), Landrace (LL), Yorkshire (YY), Liangshan (LS), and Qingyu pigs (QY) in China to identify genes which show evidence of selection during domestication. Four breed pairs, LS-YY, QY-YY, DD-YY, and LL-YY pair, were performed to detect selection signatures using the Fst method. Then we identified a list of genes that played key roles in domestication and artificial selection. For example, the PTPRM gene was shared in LS-YY, QY-YY, and DD-YY pairs and it regulates a variety of cellular processes including cell growth, differentiation as signaling molecules. The HACD3 gene was shared in QY-YY and DD-YY pairs, and the HACD3 protein is involved in the production of very long-chain fatty acids of different chain lengths. Besides, the MYH11 gene that related to muscle contraction was found in LS-YY and LL-YY pair. These results suggested that genes related to immunity, disease resistance, and metabolism were subjected to strong selection pressure in Chinese domestic pigs in the progress of domestication and evolution; however, genes related to appearance, production performance, and reproduction were undergone strong artificial selection in commercial pig breeds.

A combined GWAS approach reveals key loci for socially-affected traits in Yorkshire pigs.

Socially affected traits in pigs are controlled by direct genetic effects and social genetic effects, which can make elucidation of their genetic architecture challenging. We evaluated the genetic basis of direct genetic effects and social genetic effects by combining single-locus and haplotype-based GWAS on imputed whole-genome sequences. Nineteen SNPs and 25 haplotype loci are identified for direct genetic effects on four traits: average daily feed intake, average daily gain, days to 100 kg and time in feeder per day. Nineteen SNPs and 11 haplotype loci are identified for social genetic effects on average daily feed intake, average daily gain, days to 100 kg and feeding speed. Two significant SNPs from single-locus GWAS (SSC6:18,635,874 and SSC6:18,635,895) are shared by a significant haplotype locus with haplotype alleles 'GGG' for both direct genetic effects and social genetic effects in average daily feed intake. A candidate gene, MT3, which is involved in growth, nervous, and immune processes, is identified. We demonstrate the genetic differences between direct genetic effects and social genetic effects and provide an anchor for investigating the genetic architecture underlying direct genetic effects and social genetic effects on socially affected traits in pigs.

Improvement, identification, and target prediction for miRNAs in the porcine genome by using massive, public high-throughput sequencing data.

Despite the broad variety of available microRNA (miRNA) research tools and methods, their application to the identification, annotation, and target prediction of miRNAs in nonmodel organisms is still limited. In this study, we collected nearly all public sRNA-seq data to improve the annotation for known miRNAs and identify novel miRNAs that have not been annotated in pigs (Sus scrofa). We newly annotated 210 mature sequences in known miRNAs and found that 43 of the known miRNA precursors were problematic due to redundant/missing annotations or incorrect sequences. We also predicted 811 novel miRNAs with high confidence, which was twice the current number of known miRNAs for pigs in miRBase. In addition, we proposed a correlation-based strategy to predict target genes for miRNAs by using a large amount of sRNA-seq and RNA-seq data. We found that the correlation-based strategy provided additional evidence of expression compared with traditional target prediction methods. The correlation-based strategy also identified the regulatory pairs that were controlled by nonbinding sites with a particular pattern, which provided abundant complementarity for studying the mechanism of miRNAs that regulate gene expression. In summary, our study improved the annotation of known miRNAs, identified a large number of novel miRNAs, and predicted target genes for all pig miRNAs by using massive public data. This large data-based strategy is also applicable for other nonmodel organisms with incomplete annotation information.

Quantitative detection of thermal barrier coating parameters based on electromagnetic/capacitive dual modality sensor.

A thermal barrier coating (TBC), which is composed of a top coating (TC) and bond coating (BC), can keep a turbine engine working in high temperature. The TC is an insulated ceramic layer, and the BC is a conductive layer between the TC and engine blade. Owing to poor working conditions, some failures such as sintering, thinning of coating thickness, and oxide layer initiation will occur in the TBC. Once any part of the TBC fails, it will seriously threaten the safety of the aircraft. The quantitative detection of TBC parameters is realized with the electromagnetic/capacitive dual modality sensor in this paper. The measurement grid algorithm is used to inverse the thickness of the TC layer and the conductivity of the BC layer, and an analytical method is proposed to inverse the relative permittivity of the TC layer. According to the experiment, the inversion errors of these parameters are all less than 4%, which can meet the industry needs well.

Estimation of genetic parameters for reproductive traits in connectedness groups of Duroc, Landrace and Yorkshire pigs in China.

The objective of this study was to estimate group- and breed-specific genetic parameters for reproductive traits in Chinese Duroc, Landrace, and Yorkshire populations. Records for reproductive traits between April 1998 and December 2017 from 92 nucleus pig breeding farms, which were involved in the China Swine Genetic Improvement Program, were analysed. Due to weak genetic connectedness across all farms, connectedness groups consisting of related farms were used. Three, two and four connectedness groups for Duroc, Landrace and Yorkshire were firstly established according to the genetic connectedness rating among farms. For each connectedness group a five-trait animal model was implemented, and via restricted maximum likelihood procedure the genetic parameters were estimated for five reproductive traits i.e., total number born (TNB), number born alive (NBA), litter weight at farrowing (LWF), farrowing interval (FI) and age at first farrowing (AFF). The average of heritabilities among connectedness groups ranged from .01 (for FI in Yorkshire) to .30 (for AFF in Duroc). Estimates of repeatability for litter traits ranged from .14 to .20 and were consistent for each breed, and for FI, the estimates varied from .01 to .11 across breeds and groups. The estimated genetic correlations among litter traits (i.e., TNB, NBA and LWF) were all significantly high (>.56) and similar across breeds. Averaged genetic correlations over three breeds were -.25, -.27, -.18, -.04, -.10, -.02, and .28 for FI-TNB, FI-NBA, FI-LWF, AFF-TNB, AFF-NBA, AFF-LWF and FI-AFF, respectively. The standard errors of the estimates were all very low (<0.01) in most situations. Results from this study suggest that selection based on TNB which is currently used in dam line selection index can improve NBA and LWF simultaneously. However, care should be taken on FI and AFF as they are both greatly influenced by non-genetic factors such as management and measurement.

Analyses of Long Non-Coding RNA and mRNA profiling using RNA sequencing during the pre-implantation phases in pig endometrium.

Establishment of implantation in pig is accompanied by a coordinated interaction between the maternal uterine endometrium and conceptus development. We investigated the expression profiles of endometrial tissue on Days 9, 12 and 15 of pregnancy and on Day 12 of non-pregnancy in Yorkshire, and performed a comprehensive analysis of long non-coding RNAs (lncRNAs) in endometrial tissue samples by using RNA sequencing. As a result, 2805 novel lncRNAs, 2,376 (301 lncRNA and 2075 mRNA) differentially expressed genes (DEGs) and 2149 novel transcripts were obtained by pairwise comparison. In agreement with previous reports, lncRNAs shared similar characteristics, such as shorter in length, lower in exon number, lower at expression level and less conserved than protein coding transcripts. Bioinformatics analysis showed that DEGs were involved in protein binding, cellular process, immune system process and enriched in focal adhesion, Jak-STAT, FoxO and MAPK signaling pathway. We also found that lncRNAs TCONS_01729386 and TCONS_01325501 may play a vital role in embryo pre-implantation. Furthermore, the expression of FGF7, NMB, COL5A3, S100A8 and PPP1R3D genes were significantly up-regulated at the time of maternal recognition of pregnancy (Day 12 of pregnancy). Our results first identified the characterization and expression profile of lncRNAs in pig endometrium during pre-implantation phases.

Detection of quantitative trait loci affecting haematological traits in swine via genome scanning.

BACKGROUND: Haematological traits, which consist of mainly three components: leukocyte traits, erythrocyte traits and platelet traits, play extremely important role in animal immune function and disease resistance. But knowledge of the genetic background controlling variability of these traits is very limited, especially in swine. RESULTS: In the present study, 18 haematological traits (7 leukocyte traits, 7 erythrocyte traits and 4 platelet traits) were measured in a pig resource population consisting of 368 purebred piglets of three breeds (Landrace, Large White and Songliao Black Pig), after inoculation with the swine fever vaccine when the pigs were 21 days old. A whole-genome scan of QTL for these traits was performed using 206 microsatellite markers covering all 18 autosomes and the X chromosome. Using variance component analysis based on a linear mixed model and the false discovery rate (FDR) test, 35 QTL with FDR < 0.10 were identified: 3 for the leukocyte traits, 28 for the erythrocyte traits, and 4 for the platelet traits. Of the 35 QTL, 25 were significant at FDR < 0.05 level, including 9 significant at FDR < 0.01 level. CONCLUSIONS: Very few QTL were previously identified for hematological traits of pigs and never in purebred populations. Most of the QTL detected here, in particular the QTL for the platelet traits, have not been reported before. Our results lay important foundation for identifying the causal genes underlying the hematological trait variations in pigs.

Effects of heat shock on ovary development and hsp83 expression in Tribolium castaneum (Coleoptera: Tenebrionidae).

Heat shock affects reproductive performance in insects including Tribolium castaneum. In this study, the effects of heat shock on ovary development and hsp83 expression in T. castaneum were investigated. Two lines of T. castaneum, H line and C line, from the same base population were established and maintained for five successive generations. In each generation, the newly hatched beetles (within 3 h after eclosion) in the H line were treated with a heat shock at 40 degrees C for 1 h, and those in the C line were raised at normal temperature (30 degrees C) as control treatment. Four traits related to ovary development were measured for the beetles of the fifth generation: days from eclosion to laying the first eggs (T(o)), days from eclosion to laying the first hatchable eggs (T(h)), ovariole size on the third day after eclosion, and pupal mass of their offspring. The results showed that the beetles of the H line had a significantly longer pre-oviposition period (0.6 more days) and smaller ovariole size than those of the C line. No significant difference in pupal mass was observed. Applying heat shock to the offspring of the fifth generation of both lines led to significantly higher hsp83 expression in offspring of the C line than in offspring of the H line. Within each line, the hsp83 expression level in offspring with heat shock was significantly higher than that of offspring without heat shock, but the difference in the C line was much larger than that in the H line. We infer from these results that a tradeoff between heat resistance, registered as hsp83 expression, and ovarian development operates under heat stress in T. castaneum.

Adhesive patterns of Escherichia coli F4 in piglets of three breeds.

Escherichia coli expressing F4 fimbriae is the major pathogenic bacteria that causes diarrhea in piglets before weaning. The adhesion of E. coli to the brush borders of the epithelial cells of piglets is the precondition leading to diarrhea, which in turn is due to the presence of the F4 receptors determined by an autosomal recessive gene on the brush borders of the epithelial cells. In order to clarify the genetic mechanism of the adhesion, an in vitro adhesion experiment was carried out for three variants of E. coli F4 (ab, ac, and ad) in 366 piglets of three pig breeds [Landrace (LR), Large White (LW), and Songliao Black (SB)]. The results showed that there existed significant differences (P<0.001) in the adhesion percentage among the three breeds. Most SB piglets were nonadhesive for all the three variants, whereas most LR piglets were adhesive. Within each breed except for LR, the proportions of the three F4 variants adhering to the brush borders differed significantly. According to the patterns of the adhesion of the three F4 variants in the three breeds, it is very likely that the three F4 variants F4ab, F4ac, and F4ad have different receptors that are controlled by three different loci.

Molecular characterization, expression and mapping of porcine LMP2 and MECL-1 genes.

Low molecular mass polypeptides 2 (LMP2) and Multicatalytic endopeptidase complex subunit (MECL-1) are two of three catalytic beta-type subunits of the 20s proteasome and upon interferon gamma-induction. LMP2 is critical for the production of major histocompatibility complex (MHC) class I ligand and T-lymphocytes. The LMP2 gene is located in the MHC region, but MECL-1 is located outside the MHC region. They are involved in the antigen presentation and are important candidate genes for an initial exploration of relationships between the antigen processing genes and disease resistance. In this report, the porcine LMP2 and MECL-1 cDNA were cloned and A 5099 bp LMP2 genomic DNA structure was identified, then two single nucleotide polymorphisms were detected in the exon2 and exon5 of LMP2 gene in 367 individuals. The LMP2 and MECL-1 genes putative protein included 219,274 amino acids, respectively. Alignment and phylogenetic of predicted porcine LMP2 and MECL-1 amino acid sequence with their homologies were analyzed. Tissues expression of LMP2 and MECL-1 mRNA were observed by real time quantitative PCR (Q-PCR) method, the results revealed MECL-1 expressed widely in all tissues, but LMP2 was not detected in muscle. The porcine MECL-1 gene was mapped to chromosome 6, closely linked to microsatellite SW1108 (LOD = 4.09, 84cR) by radiation hybrid panel.

Molecular cloning, mapping, and tissue expression of the porcine cluster of differentiation 14 (CD14) gene.

CD14 plays an important role in initiating the innate response to lipopolysaccharide from Gram-negative bacteria. The gene and corresponding cDNA of porcine CD14 were sequenced and characterized. The porcine CD14 gene consists of two exons and a short intron (80 bp) located immediately after the ATG translation start codon. This structure is very similar to the CD14 gene of human, rat, mouse, rabbit, horse, and cow. The sequence of the porcine CD14 protein is 59-76% identical to that of rat, mouse, rabbit, human, horse, and cow CD14 protein. A highly conserved structure of the CD14 protein with respect to the leucine-rich repeats domain and the N-glycosylation sites was observed between species. Porcine CD14 was assigned to porcine chromosome 2q21 by a radiation hybrid panel. Using RT-PCR analysis, porcine CD14 transcripts were detected in liver, spleen, thymus, white matter, and skeletal muscle.